Radiology Department Reports Findings in Artificial Intelligence (Artificial int elligence solution to accelerate the acquisition of MRI images: Impact on the th erapeutic care in oncology in radiology and radiotherapy departments)
Radiology Department Reports Findings in Artificial Intelligence (Artificial int elligence solution to accelerate the acquisition of MRI images: Impact on the th erapeutic care in oncology in radiology and radiotherapy departments)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Caen, F rance, by NewsRx journalists, research stated, "MRI is essential in the manageme nt of brain tumours. However, long waiting times reduce patient accessibility." The news reporters obtained a quote from the research from Radiology Department, "Reducing acquisition time could improve access but at the cost of spatial reso lution and diagnostic quality. A commercially available artificial intelligence (AI) solution, SubtleMR™ can increase the resolution of acquired images. The ob jective of this prospective study was to evaluate the impact of this algorithm t hat halves the acquisition time on the detectability of brain lesions in radiolo gy and radiotherapy. The T1/T2 MRI of 33 patients with brain metastases or menin giomas were analysed. Images acquired quickly have a matrix divided by two which halves the acquisition time. The visual quality and lesion detectability of the AI images were evaluated by radiologists and radiation oncologist as well as pi xel intensity and lesions size. The subjective quality of the image is lower for the AI images compared to the reference images. However, the analysis of lesion detectability shows a specificity of 1 and a sensitivity of 0.92 and 0.77 for r adiology and radiotherapy respectively. Undetected lesions on the IA image are l esions with a diameter less than 4mm and statistically low average gadolinium-en hancement contrast."
Key words
Caen/France/Europe/Artificial Intelli gence/Drugs and Therapies/Emerging Technologies/Health and Medicine/Machine Learning/Oncology/Radiology/Radiotherapy
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